Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77337
Title: Improving semantic relevance for sequence-to-Sequence learning of Chinese social media text summarization
Authors: Ma, S
Sun, X
Xu, J
Wang, H
Li, W 
Su, Q
Issue Date: 2017
Source: ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 30 Jul - 4 Aug 2017, v. 2, p. 635-640
Abstract: Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.
Publisher: Association for Computational Linguistics (ACL)
ISBN: 9781945626760
DOI: 10.18653/v1/P17-2100
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

22
Citations as of Aug 28, 2020

WEB OF SCIENCETM
Citations

10
Last Week
0
Last month
Citations as of Nov 6, 2020

Page view(s)

165
Citations as of Nov 22, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.